Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

Artificial perception for robots operating in outdoor natural environments, including forest
scenarios, has been the object of a substantial amount of research for decades. Regardless …

Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …

Existing deep learning-based depth completion methods generally employ massive stacked
layers to predict the dense depth map from sparse input data. Although such approaches …

Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

Depth completion is a vital task for autonomous driving as it involves reconstructing the
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …

Multi-modality image fusion is a technique that combines information from different sensors
or modalities enabling the fused image to retain complementary features from each modality …

The encoder-decoder network (ED-Net) is a commonly employed choice for existing depth
completion methods but its working mechanism is ambiguous. In this paper we visualize the …

Existing panoramic depth estimation methods based on convolutional neural networks
(CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures …

Guided depth completion aims to recover dense depth maps by propagating depth
information from the given pixels to the remaining ones under the guidance of RGB images …